Machine Learning is a type of scientific process in the computer world that's designed to help computers recognize and respond to patterns of information - and no-one knows information quite like Google. With the right machine learning strategy, businesses can unlock endless amounts of new potential for their enterprise, helping them to make informed decisions for the future. Today's developers are using machine learning as a way of creating apps that predict exactly what their users need before they have to ask for it.
If machine learning is all about figuring out how businesses can respond proactively to the data that they collect, Google Machine learning is all about making data management easier for the masses. For instance, some of the most common uses of machine learning extend all the way from analyzing common reasons for customer churn to understanding why a product fails before it has a chance to break down.
Machine learning applications can monitor computer logs, diagnose anomalies that might indicate the presence of fraud, or help companies to personalize the content that they deliver to their customers to improve client satisfaction. However, tapping into the full potential of machine learning by yourself, as a budding developer, would be incredibly complex.
Google Machine Learning is the 'come one, come all' approach to entering the era of digital transformation. Designed to effectively manage any data, of any size, the GCP has developed a managed service that enables today's companies to build their own models that according to their specific needs and goals. Regardless of whether you're looking for help with Google Cloud Speech, or contextually organizing photos, the Machine Learning Engine can transform any TensorFlow model into a solution of continuous education.
Situated on the expansive Google Cloud, the Google Machine Learning service simplifies the process of unlocking AI potential. You don't have to worry about setting up and managing your own hardware, and you don't need to hire an expert to set up complex processing systems that can deal with huge amounts of data on-premise. Instead, developers have the chance to build and test machine learning models faster, and more cost-effectively than ever.
Google recommends their ML strategy for instances when:
Your data model is complicated or large
You need a large amount of training data for exceptional learning
You need incredible speed to improve performance in consumer apps.
The Ability to Use Scalable Predictive Analytics: You can transition from training to prediction and prediction batch services quickly and seamlessly, using Google load balancing to evolve at a rate that suits you.
Simplicity: The presence of "HyperTune" means that developers can tune their model training methods to their needs, using pre-established methods for optimization. You can also manage thousands of different experiments in the cloud - keeping cost to a minimum. Developers can establish models using the data lab, while scientists analyze and understand the information produced, creating their own TensorFlow model maps.
Deep learning: If you want to take your AI to the next level, cloud machine learning from Google supports anything built with TensorFlow, so you can design an application that's perfectly suited to your specific type of data, and created to work within a variety of complex scenarios.
While Google has already achieved something of a reputation for their willingness to experiment with new and disruptive technologies, it's become more obvious throughout the years that "data" will always be a point of concentration for the search giant. Google Cloud created Cloud Machine Learning to serve developers who want to create their own customized solutions to complex problems. The Google Machine Learning Platform is:
Integrated: it works perfectly alongside existing Google services like Cloud Dataflow, Cloud Storage, and Cloud Datalab.
Straightforward: You can build and tune your own hyper-parameters easily with the help of "HyperTune", so developers don't have to spend hours discovering values best suited to their model.
Managed: You can focus your efforts and budgets on prediction and development without having to worry about infrastructure.
Scalable: Create models of any size, for any kind of data using the distributed training infrastructure which supports GPUs and CPUs. You can also accelerate model development by training across several nodes.
Portable: Developers can use the TensorFlow opensource framework to educate models on sample sets of data, before using Google Cloud Platform for at-scale training. You can then download those models for mobile integration or local execution.
The Cloud Machine Learning Strategy from Google is built on the open-source TensorFlow framework, which allows developers to train their own AI models using customized graphs. TensorFlow offers online and batch prediction at scale, with systems that are perfect for everything from numbers, to video, text, vision, and sound.
TensorFlow was an obvious choice for the machine learning framework because the system was developed by the Google Brain Team, using Google Machine Intelligence research. Today, the system is integrated into more than 100 existing Google Services.
The Cloud ML solution runs TensorFlow to assess huge amounts of data at once. With it, developers can access "regression" and "classification" models for their machine learning frameworks.
As we continue to explore the possibilities of AI and digital transformation, it makes sense that more companies and developers would begin to experiment with the possibilities that machine learning offers. As we look ahead, Google's vision for machine learning in the current marketplace is focused on producing positive social, and economic changes, which means that we could see the ML platform becoming bigger and better than ever.
According to experts behind the Google brand, machine learning is the feature that will drive the next wave of cloud computing. After all, in this world of big data, the easiest way to respond to the needs of ever-changing companies and markets, is to create a system that can analyze and respond to queries and concerns in real time.
For help tapping into the benefits of machine learning and the Google Cloud Platform in your industry, reach out to Coolhead Tech for your personalized tour of GCP.